Comparison of Bio-inspired Algorithms Applied to the Hospital Mortality Risk Stratification

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 181)


The construction of patient classification (or risk adjustment) systems allows comparison of the effectiveness and quality of hospitals and hospital services, providing useful information for management decision making and management of hospitals. Risk adjustment systems to stratify patients’ severity in a clinical outcome are generally constructed from care variables and using statistical techniques based on logistic regression (RL). The objective of this investigation is to compare the hospital mortality prediction capacity of an artificial neural network (RNA) with other methods already known.


Hospital mortality Risk stratification Intensive care unit Artificial neural networks Bootstrap 


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Universidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Universidad Simón BolívarBarranquillaColombia
  3. 3.Universidad de La Costa (CUC)BarranquillaColombia
  4. 4.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras
  5. 5.Corporación Universitaria Minute de Dios. UNIMINUTOBarranquillaColombia
  6. 6.Corporación Universitaria LatinoamericanaBarranquillaColombia

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